Data analytics is used in many industries to allow companies and organization to make better business decisions and in the sciences to model and verify theories. There are many tools available for processing and managing data. Examples of data processing and management tools include database tools, visual tool for creating, analyzing, and communicating decision models, spreadsheet programs, etc. Thus, there are many tools that may use tables or other grid data sources. Moreover, visualization is often needed for different types of data sources, whether they be spreadsheet data, data in a CSV file, data in a SQL table, data in some other data base, data in a cube, or data in some other structured electronic storage container.
A spreadsheet is one example of a grid data source that may be used to create a table which displays numbers in rows and columns. Spreadsheets can be used for a variety of purposes. For example, spreadsheets are often used in accounting, budgeting, charting/graphing, financial analysis, scientific applications, etc. Spreadsheets can exist in paper format, but are more commonly today provided using electronic spreadsheet tools. Electronic spreadsheets are frequently used to manipulate, condense and organize vast collections of data. Moreover, spreadsheets have the ability to re-calculate the entire spreadsheet automatically after a change to a single cell is made, which saves save users a tremendous amount of time. While the data analytic tools, such as the spreadsheet, have become ubiquitous in every organization and will likely remain so, the quality of information visualization has not kept pace.
After data has been collected and arranged or entered into a tool, such as a spreadsheet, compelling stories based on the data cannot be communicated effectively without using charts and other visualizations. In information visualization, as the volume and complexity of the data increases, researchers require more powerful visualization tools that enable them to more effectively explore multidimensional datasets. The most common visualization involves the use of charts to convey information about data. However, a given data type may have several different visual representations at the user's disposal.
Currently, users may select the data to include in a chart, and then select the chart type. This may be frustrating to users that do not understand the difference between the choices available. Thus, a user that does not know what chart type is the most suitable for what the user wants to convey may create charts based on what the user thinks they like or based on what the user is familiar with. As a result, the chart or visualization may not convey the information as intended or in a most useful manner because the data may not be properly mapped to the chart's construct. Today, there is not a chart recommendation tool that provides the user with optimal chart choices in a ranked order based on an analysis of the data or that guides users to make better choices in creating visualizations.